Open AccessDigital refocusing based on deep learning in optical coherence tomography
Author(s) -
Zhuoqun Yuan,
Di Yang,
Zihan Yang,
Jingzhu Zhao,
Yanmei Liang
Publication year - 2022
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.453326
Subject(s) - optical coherence tomography , deep learning , artificial intelligence , computer science , computer vision , coherence (philosophical gambling strategy) , focus (optics) , imaging phantom , pixel , optics , generative adversarial network , physics , quantum mechanics
We present a deep learning-based digital refocusing approach to extend depth of focus for optical coherence tomography (OCT) in this paper. We built pixel-level registered pairs of en face low-resolution (LR) and high-resolution (HR) OCT images based on experimental data and introduced the receptive field block into the generative adversarial networks to learn the complex mapping relationship between LR-HR image pairs. It was demonstrated by results of phantom and biological samples that the lateral resolutions of OCT images were improved in a large imaging depth clearly. We firmly believe deep learning methods have broad prospects in optimizing OCT imaging.